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Semantic Segmentation

Papers

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Showing 6601–6650 of 14763 papers

TitleStatusHype
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations—0
CircleSnake: Instance Segmentation with Circle RepresentationCode0
MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road ScenesCode0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Deep Learning for Global Wildfire Forecasting—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation—0
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
Two-Level Temporal Relation Model for Online Video Instance SegmentationCode0
SL3D: Self-supervised-Self-labeled 3D RecognitionCode0
Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation—0
Saliency Can Be All You Need In Contrastive Self-Supervised Learning—0
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion SegmentationCode1
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and TrackingCode1
IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernelsCode0
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks—0
Impact of PolSAR pre-processing and balancing methods on complex-valued neural networks segmentation tasksCode1
Grafting Vision Transformers—0
Localized Randomized Smoothing for Collective Robustness Certification—0
Object Segmentation of Cluttered Airborne LiDAR Point CloudsCode1
Weakly Supervised Semantic Segmentation of Echocardiography Videos via Multi-level Features Selection—0
Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes—0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image SegmentationCode0
Class Based Thresholding in Early Exit Semantic Segmentation Networks—0
Efficient few-shot learning for pixel-precise handwritten document layout analysis—0
Open-vocabulary Semantic Segmentation with Frozen Vision-Language ModelsCode1
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture—0
Fast and Efficient Scene Categorization for Autonomous Driving using VAEs—0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI Using Deep Learning—0
Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception—0
How precise are performance estimates for typical medical image segmentation tasks?—0
Boosting Semi-Supervised Semantic Segmentation with Probabilistic RepresentationsCode1
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency GuidanceCode0
RGB-T Semantic Segmentation with Location, Activation, and SharpeningCode1
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic SegmentationCode1
MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentationCode1
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and BenchmarksCode1
Learning Explicit Object-Centric Representations with Vision Transformers—0
ConnectedUNets++: Mass Segmentation from Whole Mammographic Images—0
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled DataCode1
Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping—0
Towards an efficient Iris Recognition System on Embedded Devices—0
BARS: A Benchmark for Airport Runway SegmentationCode1
Large Batch and Patch Size Training for Medical Image Segmentation—0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation—0
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingCode1
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4InternImage-HValidation mIoU62.9—Unverified
5M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified